In this section, we borrow the idea of stability variable selection and propose a robust change point detection framework, named stability detection. Stability selection was firstly advocated by ([1]}) to enhance the robustness and control the false discovery rate of variable selection. Half of the samples are randomly selected to feed into a base model at each iteration. The relevant variables are ultimately discovered based on the votes aggregated over all the variable selection results. Later on, ([2]}) extent the stability selection by sampling disjoint subsets of samples.
